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The A-Z Guide to Deep Learning is designed to provide a comprehensive roadmap for both beginners and experienced practitioners seeking to delve into the realm of Deep Learning. Whether you're just starting your journey or looking to expand your expertise, this repository offers a wealth of resources to support your learning and exploration.

1- Extensive Coverage: Explore a wide range of topics, including fundamental concepts, advanced algorithms, statistical methods, and practical techniques crucial for understanding and implementing Deep Learning models.

2-Hands-On Implementations: Dive into practical implementations of Deep Learning algorithms and techniques using Python, alongside detailed explanations, code examples, and real-world applications.

3-Progressive Learning Path: Follow a structured learning path that progresses from foundational concepts to advanced topics, ensuring a gradual and comprehensive understanding of Deep Learning principles and methodologies.

4-Supplementary Resources: Access supplementary materials, such as articles, tutorials, research papers, and curated datasets, to enrich your learning experience and stay updated with the latest developments in Deep Learning.

Fundamental Concepts: Covering essential concepts such as neural networks, activation functions, optimization algorithms, loss functions, and regularization techniques.

Advanced Algorithms: Exploring advanced Deep Learning architectures and algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and reinforcement learning.

Statistical Methods and Techniques: Discussing statistical methods and techniques commonly used in Deep Learning, such as hypothesis testing, probability distributions, dimensionality reduction, and Bayesian inference.

1- Share Your Expertise: If you have knowledge or insights in Deep learning , your contributions can assist others in learning and applying these concepts.

2-Enhance Your Skills: Contributing to this project offers a great opportunity to deepen your understanding of Deep learning . Writing, coding, or reviewing content will reinforce your knowledge while uncovering new areas of the field.

3- Collaborate and Connect: Join a community of like-minded individuals committed to advancing AI education. Work with peers, receive feedback, and build connections that may open up new opportunities.

4- Make a Difference: Your contributions can shape how others learn and engage with machine learning. By refining and expanding content, you help shape the education of future engineers and AI experts.

🚀 Fork & Star this repository

👩‍💻 Explore and Learn from structured lessons

🔧 Enhance the current blog or code, or write a blog on a new topic

🔧 Implement & Experiment with provided code

🤝 Collaborate with fellow DL enthusiasts

📌 Contribute your own implementations & projects

📌 Share valuable blogs, videos, courses, GitHub repositories, and research websites

Please enrolled in the following courses to strengthen knowledge and practical skills in Deep Learning. These courses are designed to provide both theoretical understanding and hands-on experience with real-world DL applications.

🔗 Improving Deep Neural Networks!

1- Covers foundational concepts such as Optimization Algorithms,Hyperparamter tunning etc.

🔗 Deep Learning- Neural Network

1- Focuses Funcation Concept of deep learning ,such as ,Deep learning, ANN etc

💡 These courses are part of a structured Deep Learningcurriculum offered by Coursera, designed by Coursera team, and emphasize practical implementation using Python and deep learning libraries.

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Course 1 - 🧠Deep Learning-Neural Networks

Week 1-📚Chapter1: Introduction of Deep learning

Topic Name/Tutorial	Video	Code	Todo list
✅1-Understanding Basic Neural Networks g	1-2-3-4-5	Content 3
✅2-Supervised Learning with Neural Networks⭐	1	Content 6
✅3-Exploring the Different Types of Artificial Neural Networks⭐	-1	---
✅4- Why is Deep Learning taking off?⭐	1	---
✅5-Best Free Resources to Learn Deep learning (DL)⭐	---	---
✅6-GPU-CPU-TPU⭐	---	---	write blog

Week 2-📚Chapter1:2 Logistic Regression as a Neural Network

Topic Name/Tutorial	Video	Notebook
✅1- Binary Classification-s	1	Content 3
✅2- Logistic Regression-s	1-2	Content 6
✅3- Understanding the Logistic Regression Cost Function-S	1	---
✅4-Understanding the Logistic Regression Gradient Descent-s	1-2	---
✅5-Intuition about Derivatives	1
✅6-Computation Graph⭐	1-2	---
✅*7-Derivatives with a Computation Graph	1	---
✅8-Logistic Regression Gradient Descent⭐	1	---
✅9-Gradient Descent on m Examples⭐	1

Week 3-📚Chapter 3 Python and Vectorization

Topic Name/Tutorial	Video	Notebook
✅1-Vectorization⭐	1
✅2-More Vectorization Examples⭐	1
✅3-Vectorizing Logistic Regression⭐	1
✅4-Vectorizing Logistic Regression’s Gradient Output⭐	1

Week 4-📚Chapter4: Shallow Neural Network

Topic Name/Tutorial	Video	Notebook	Extra Reading
✅1-Neural Networks Overview⭐	1-2		Tiny Neural Networks-Paper
🌐2-Neural Network Representation⭐	1
🌐3-Computing a Neural Network's Output⭐	1-2
🌐4-Vectorizing Across Multiple Examples	1
🌐5-Explanation for Vectorized Implementation	1
🌐6-Activation functions-Copy fro courseteach	1
🌐7-Why do you need Non-Linear Activation Functions?	1
🌐8-Derivatives of Activation Functions?	1
🌐9-Gradient Descent for Neural Networks?	1
🌐10-Backpropagation Intuition?	1
🌐11-Random Initialization?	1
🌐12-NoProp, does not even require a Forward pass?🧠✨	1

Week 5-📚Chapter5:Deep Neural Network

Topic Name/Tutorial	Video	Notebook
🌐1-Deep L-layer Neural Network	1
🌐2-Forward Propagation in a Deep Network	1
🌐3-Getting your Matrix Dimensions Right	1
🌐4-Why Deep Representations?	1
🌐5-Building Blocks of Deep Neural Networks?	1
🌐6-Forward and Backward Propagation?	1
🌐7-Parameters vs Hyperparameters	1

Course 2 - 🧠Improving Deep Neural Network

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code	Exra Resoruces
🌐1-Train / Dev / Test sets	1
🌐2-Bias Variance	1
🌐3-Basic Recipe for Machine Learning	-1
🌐4- Regularization⭐	1-2
🌐5-Why Regularization Reduces Overfitting	1-2	---
🌐6- Dropout Regularization	1-2-3
🌐7- Other Regularization Methods	1-2
🌐8- Normalizing Inputs	1
🌐9- Vanishing-Exploding Gradients	1		Doc
🌐10- Weight Initialization for Deep Networks	1
🌐11- Numerical Approximation of Gradients	1
🌐12- How Gradient Checking Can Save You Time and Help Debug Neural Networks	1-2

Week 2-📚Chapter2:Optimization Algorithms

Dive deeper into neural network optimization techniques in Week 2 of our Deep Learning series. This chapter covers key optimization algorithms that help accelerate and stabilize training, with hands-on videos, Medium tutorials, and Colab notebooks for each concept.

Topic Name/Tutorial	Video	Code	Extra Reading
1-Mini-batch Gradient Descent⭐	1
🌐2-Understanding Mini-batch Gradient Descent⭐	1
🌐3-Exponentially Weighted Averages⭐	1-2
🌐4-Understanding Exponentially Weighted Averages⭐	1
🌐5-Bias Correction in Exponentially Weighted Averages⭐	1
🌐6-Gradient Descent with Momentum⭐	1
🌐7-RMSprop⭐	1-2
🌐8-Adam Optimization Algorithm	1		1
🌐9-Learning Rate Decay	1
🌐10-The Problem of Local Optima	1

Week 3-📚Chapter3:Hyperparameter tunning , Batch Normalization and Programming Frameworks

Topic Name/Tutorial	Video	Code	Note	Difficulty level
1-Tuning Process	1		---	Intrmediate
2-Using an Appropriate Scale to pick Hyperparameters	1		---	Intrmediate
3-Hyperparameters Tuning in Practice Pandas vs Caviar	1		LINK	Intrmediate
4-Normalizing Activations in a Network	1		LINK	Intrmediate
5-Fitting Batch Norm into a Neural Network	1		LINK	Intrmediate
6-Why does Batch Norm work	1		Note	Intrmediate
7-Batch Norm at Test Time	1		Note	Intrmediate
8-Softmax Regression	1		Note	Intrmediate
9-Softmax Training	1		Note	Intrmediate
10-Deep Learning Frameworks	1		Note	Intrmediate

Course 3 - 🧠Structuring Machine Learning Projects

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code
🌐1-Train / Dev / Test sets	1

Course 4 - 🧠Convolutional Neural Networks

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code
🌐1-Train / Dev / Test sets	1

Course 5 - 🧠Sequence Models

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code
🌐1-Train / Dev / Test sets	1

Course 5 - 🧠Graph Neural Networks

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code
🌐1-Train / Dev / Test sets	1

Course 6 - 🧠Autoencoders

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code	Extra Resources
🌐1-Introduction to Autoencoders	1		1

Course 7 -⚡ Transformers

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code	Extra Resources
🌐1-Introduction to Transformers	1		1

Course 7 -Transfer Learning and Distillation

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code	Extra Resources
🌐1-Transfer Learning	1		1

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Deep Learning")

• Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
• You can only work on issues that have been assigned to you.
• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
• If you have modified/added code work, make sure the code compiles before submitting.
• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
• Do not update the README.md.

We would love your help in making this repository even better! If you know of an amazing AI course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! 🚀

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀